The problem
01 / 07 — PainBefore touching RAG, understand what breaks without it. The pattern: LLM asked about private data → hallucinates, refuses, or simply lies with confidence.
You ask an LLM about your company's internal policy, last quarter's metrics, or a document it has never seen. It either hallucinates an answer, or says "I don't know." Both are useless in production.
LLMs are frozen at training time. The world keeps moving. Your internal data — policies, reports, code, tickets — was never in the model to begin with.
You can't paste your 10,000-page knowledge base into a prompt. Even if you could — every token costs money, adds latency, and dilutes focus.
Without grounding, the model fills gaps with plausible-sounding fiction. It doesn't signal uncertainty — it's confident and wrong, which is the worst kind of wrong.
Retraining costs $10,000–$1M+. It takes weeks. The model still can't cite sources. And the moment your data changes, you repeat everything.
What if, before generating an answer, the model first retrieved the relevant facts from your knowledge base? That's RAG — grounding generation in retrieval. Not retraining. Not context stuffing. Retrieve, then generate.
How it works
02 / 07 — MechanismRAG has two distinct phases — offline indexing and online retrieval+generation. Most production failures happen when engineers treat them as one thing.
The embedding model is used twice — once offline to encode documents, once online to encode the query. They must be the exact same model. Swap one without re-indexing and the vector spaces become incompatible. Every similarity score becomes noise. The system appears to run but retrieves garbage.
Pipeline deep dive
03 / 07 — PipelineSix steps. Click each one to understand what happens inside — and what specifically breaks.
Live simulator
04 / 07 — DemoWatch every step of the pipeline execute in real time. Edit the query, observe how similarity scores shift.
Trade-offs
05 / 07 — Trade-offsRAG solves real problems and introduces new ones. The honest account — no cheerleading.
- Real-time knowledge updates — no retraining
- Citable, source-grounded answers
- Dramatically lower hallucination rate
- Works with proprietary / private data
- Decomposable — swap embedding model, vector DB, or LLM independently
- Two failure surfaces: retrieval and generation
- Garbage chunking = garbage retrieval = garbage answers
- Chunk boundaries destroy document context
- Added latency: embed + search + generate
- Semantic similarity ≠ logical relevance
RAG vs alternatives
| Approach | Update speed | Cost | Accuracy | Best for |
|---|---|---|---|---|
| RAG | Minutes | Low | High | Dynamic docs, Q&A, support bots |
| Fine-tuning | Weeks | Very high | Medium | Style / tone / domain adaptation |
| Full context | Instant | Very high | Medium | Small doc sets (<50 pages) |
| Keyword search | Real-time | Very low | Low | Exact term lookup, legal clauses |
Teams choose RAG because it's trendy, not because it's appropriate. If your knowledge base has fewer than 1,000 chunks, you might be better off with full-context stuffing or a hybrid keyword+vector approach. Know your data before architecting.
Failure modes
06 / 07 — FailuresEach failure below is real, common, and usually discovered in production — not in the demo. Click to reveal root cause and fix.
Predict & verify
07 / 07 — QuizPrediction builds real understanding. Reason through each question before selecting an answer. Surface learners can follow tutorials. Deep learners can predict what breaks and when.
After mastering the basics: study HyDE (hypothetical document embeddings), re-ranking with cross-encoders, multi-vector retrieval (ColBERT), and hybrid BM25+vector fusion. These are the patterns that separate production RAG from demo RAG.